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Related Experiment Video

Updated: Jul 7, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

ATM communications network control by neural networks.

¹Commun. Switching Lab., NTT, Tokyo.

IEEE Transactions on Neural Networks

|January 1, 1990

Summary

This study introduces a neural network approach for managing asynchronous transfer mode (ATM) network traffic. The proposed method effectively controls service quality despite unknown traffic patterns and changing requirements.

Area of Science:

Computer Science
Telecommunications Engineering
Artificial Intelligence

Background:

Controlling network traffic in asynchronous transfer mode (ATM) networks is challenging due to unpredictable traffic patterns and dynamic service quality requirements.
Traditional network controllers struggle to adapt to these changing conditions, leading to inefficiencies.

Purpose of the Study:

To develop an adaptive neural network-based learning method for effective service quality control in ATM networks.
To address the difficulties in building efficient network controllers for dynamic communication environments.

Main Methods:

Utilized backpropagation neural networks to learn the complex relationships between offered traffic and service quality.
Proposed a novel training data selection technique, the leaky pattern table method, for precise neural network learning.

Related Experiment Videos

Last Updated: Jul 7, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Evaluated the controller's performance through simulations of basic call admission models.

Main Results:

The proposed neural network controller demonstrated adaptability and ease of implementation.
The leaky pattern table method facilitated accurate learning of traffic-service quality relations.
Simulation results indicated effective performance in basic call admission scenarios.

Conclusions:

Neural networks offer a viable and adaptive solution for service quality control in ATM networks.
The proposed leaky pattern table method enhances the precision of neural network learning for network traffic management.
This approach provides a robust framework for intelligent network control in dynamic communication environments.